Climate change is a source of severe stress for crops and reduces agricultural yields worldwide, with the Mediterranean region experiencing some of the most intense impacts. With a growing global population, agriculture faces the dual challenge of mitigatinge the adverse effects of climate change while meeting increasing food demands. One important challenge in eggplant (Solanum melongena) cultivation is the disruption of the plant's reproductive development under high temperatures. Eggplant exhibits andromonoecy, a sexual system where hermaphrodite and male flowers coexist on the same plant. Specifically, eggplant displays functional andromonoecy, where male flowers retain a non-functional pistil. Different eggplant varieties display different proportions of hermaphrodite and male flowers, but it has been shown that the proportion of male flowers always increases under heat stress conditions—a response aimed at conserving plant resources but resulting in yield reduction for farmers. It has been suggested that the balance between male and hermaphrodite flowers is linked to stress and resource allocation. Therefore, under stressful conditions, eggplants are thought to produce more male flowers, because they require fewer resources and avoid the metabolic cost of fruit development. Nevertheless, this hypothesis has not been systematically tested so far.

In this study, we investigated the impact of heat stress on the production of hermaphrodite and male flowers in the experimental eggplant variety “Micromel”, developed in our laboratory, and commercial varieties from Valencia, Spain, that were grown in the field. Furthermore, we assessed whether biostimulants derived from plant organic matter and enriched with aminoacids, applied during the vegetative phase, could improve plant resilience, thus maintaining fruit set under heat stress conditions

This study also explored the above hypothesis by testing the effect of the application of a biostimulant on flower sex and fruit set. For this, we used the dwarf eggplant variety (“Micromel”), developed in our laboratory. Micromel plants treated with the biostimulant during the vegetative phase produced significantly more hermaphrodite flowers and higher fruit yields than control plants, supporting the idea that the andromonoecy strength is controlled by resource availability.

To assess the impact of heat stress on andromonoecy, a subset of plants were exposed to heat stress (35ºC day/30ºC night). While the control plants under heat stress ceased producing hermaphrodite flowers, the biostimulant-treated plants continued to produce them. Although the fruit set was limited in the biostimulant-treated plants due to reduced pollen viability at high temperatures, these plants showed greater resilience to stress, suggesting a faster recovery post-stress. Additionally, when subjected to secondary stresses like drought, the biostimulant-treated plants exhibited enhanced tolerance.

Overall, these results support the idea that the strength of andromonoecy is linked to resource allocation in eggplant and that biostimulant application may support eggplant yield by promoting flower fertility and fruit set and improving stress tolerance. This approach offers a promising strategy for crop management in the face of climate change.

The application of LiDAR (Light Detection and Ranging) technology in forestry has been steadily increasing, revolutionizing how forest ecosystems are monitored and managed.

This study leverages GIS and LiDAR flight data to assess dendrometric parameters on a tree-by-tree basis in Mont Avic Regional Park, located in the Autonomous Aosta Valley Region (NW Italy). The LiDAR data, collected during 2020-2021, was processed to derive Digital Terrain Models (DTM) and Digital Surface Models (DSM), enabling the computation of a Canopy Height Model (CHM) with a Ground Sampling Distance (GSD) of 0.5 m. Although an optimal CHM for forestry dendrometric assessments typically has a GSD of around 0.15-0.25 m, the high number of LiDAR returns, exceeding 12 per square meter, compensated for this issue. The CHM was segmented using a local maxima algorithm to delineate individual tree crowns. This study specifically focused on vertical biomass (VB) assessment, utilizing the 0.5 m resolution CHM to segment canopies and calculate species-specific incidence areas and related diameters through empirical formulas. These formulas, derived from similar regions and documented in literature, correlated tree crown measurements from GIS with diameters at 1.30 m for each species. Dendrometric formulas were applied to estimate tree volume, with validation performed using ground measurement data from randomly selected, evenly distributed areas within the study site. For each tree, the following parameters were obtained: height (H), crown area (C), diameter (D), volume (V), altitude (A), coordinates (X and Y in ED50 UTM 32N), tree species, and forestry category from species maps.

In conclusion, this integrated approach combining advanced remote sensing technologies with GIS underscores the potential for comprehensive forest ecosystem monitoring and management as well as in agro-forestry. Future perspectives include monitoring fire vulnerability through the Vegetation Health Index (VHI) and analyzing trends to support agro-forestry planning and management.

Lead pollution poses a formidable threat to agriculture, bioaccumulating in crops and ultimately harming human health. Even low-level exposure can significantly reduce crop yields, diminish their nutritional value, and precipitate economic losses and food insecurity. To combat this threat, we investigated the efficacy of a novel biostimulant, Lysinibacillus fusiformis US3, which was previously isolated from the rhizosphere of a plant with enhanced plant growth attributes to promote ecorestoration of a lead-remediated soil. Four lead-resistant bacterial (LRB) strains, Bacillus infantis K66, Halopseudomonas xiamenensis B13, Lysinibacillus fusiformis KAF67, and Pseudomonas spp. A27, harbouring the gene cluster PbrABCT were employed in the treatment of lead-contaminated soil. The treatment efficacy was remarkable, with final lead removal percentages of 85%, 82%, 83%, and 83%, respectively, while the control achieved a 41% removal rate. To achieve ecorestoration and facilitate the agricultural reuse of soil, maize seeds from the Agricultural Development Program, Nigeria, were planted in the treated soil, and 10% w/v of the US3 biostimulant was introduced as liquid culture into the pots except in the control pot. The inoculum concentration was determined according to the McFarland standard and plant growth parameters such as shoot length, root length, and fresh and dry root weight, which were monitored for 28 days under greenhouse conditions. The post-cultivation analysis revealed enhanced plant growth and biomass yield in US3-inoculated pots; a 48% lead uptake by the maize in control pots, while lead was not detected in the inoculated pots; and 53% residual lead in the control soil, while lead was not detected in the inoculated soil. The synergistic application of lead-resistant bacteria and the US3 biostimulant effectively ecorestored lead-stressed soil, demonstrating a promising approach for sustainable lead mitigation. This study highlights the potential of microbial solutions for environmental remediation and agricultural sustainability.

Botrytis cinerea, the primary cause of gray mold in vineyards, significantly impacts the wine industry by reducing both yield and quality. While chemical fungicides remain the primary control method, their use has led to pathogen resistance and raised concerns about toxicity to human health and the environment. The demand for zero-residue wines necessitates alternative approaches. Natural bioactive products (NBPs) offer a promising solution by inhibiting pathogen growth and reproduction. However, their field application is challenging due to instability, solubility issues, and lack of specificity. Nanoencapsulation can enhance NBP efficacy, but the development and application of nanocarriers (NCs) require a multidisciplinary approach to overcome scientific and technical hurdles. This study presents the results of in vitro, ex-situ, and field application tests against B. cinerea using Rubia tinctorum and Uncaria tomentosa extracts encapsulated in chitosan-based NCs. Laboratory tests revealed high efficacies, with minimum inhibitory concentrations (MICs) of 250‒375 μg/mL in range and complete protection of artificially inoculated 'Tempranillo' grapes at MIC doses. Field trials conducted in a D.O. Ribera de Duero vineyard during the 2024 growing season showed promising results, with no signs of phytotoxicity and no adverse effects on grape must quality parameters. These findings suggest that biopolymeric NCs offer a non-toxic and eco-friendly platform for delivering NBPs without compromising wine quality.

Hyperspectral remote sensing using Unmanned Aerial Vehicles (UAVs) provides accurate, near-real-time, and large-scale spatial estimation of leaf area index (LAI), a very important crop variable used for monitoring crop growth. In the present study, the LAI of wheat crops was estimated using high-resolution UAV-borne hyperspectral data with a spectral range of 400-1000nm and a spatial resolution of 4cm. A total of twenty-seven hyperspectral vegetation indices were computed. The PLS (Partial Least Squares) regression combined with the VIP (Variable Importance in the Projection) scores were used for selecting the optimum indices as feature vectors for the Extreme Gradient Boosting (Xgboost) model for predicting LAI. The twelve optimal vegetation indices with VIP scores above 1 were selected to develop the prediction model. Once validated against the in situ-measured LAI values, the prediction model showed good accuracy, with R² of 0.71, RMSE of 0.52, and MAE of 0.44. The model was used to generate a spatial map showing the variability in the LAI of wheat fields. Accurate mapping of LAI for wheat crops was achieved by integrating high-resolution UAV data and machine learning models. The results can be up-scaled to farmers’ fields for the operational delivery of LAI of crops to monitor crop growth and predict yield.

Camellia japonica (common camellia or Japanese camellia) has long been valued in Eastern medicine and cosmetics for its rich bioactive compounds, known for their antioxidant, antimicrobial, anti-inflammatory, and anticancer properties. This study investigated the antibacterial potential of hydromethanolic extracts from leaves and flowers of the 'Lipstick' cultivar against two significant phytopathogens: Erwinia amylovora (EA) and Xanthomonas campestris pv. campestris (Xcc). Gas chromatography-mass spectrometry analysis revealed the primary constituents in the leaf extract to include D-fucose, dihydroxyacetone, methoxy-phenyl-oxime (MPO), 2,3-dihydro-3,5-dihydroxy-6-methyl-4H-pyran-4-one (DDMP), and 1-(4-hydroxy-3,5-dimethoxy phenyl)-ethanone. The flower extract shared MPO and DDMP as main phytochemicals, along with diethoxyacetic acid ethyl ester, nonanoic acid, 1,2-cyclopentanedione, and eicosane. In vitro assays demonstrated low activity for the leaf extract and minimum inhibitory concentration (MIC) values of 1000 and 1250 μg/mL against Xcc and EA, respectively, for the flower extract. At these concentrations, the flower extract achieved complete inhibition of biofilm formation and, in the case of EA, substantial reduction in amylovoran production. Moreover, in vivo tests on artificially-infected Pyrus communis L. branches showed effective control of fire blight at a concentration of 1250 μg/mL. These findings highlight the potential of C. japonica flower extracts as eco-friendly biorationals for protecting crops against bacterial phytopathogens, particularly in the management of fire blight in pear trees.

Drone-assisted crop growth monitoring has significantly boosted the demand for precision agriculture in recent years. Different vegetation spectral indices derived from drone-based multispectral images could be found more appropriate, as well as near-real-time monitoring tools over traditional methods and satellite remote sensing. The present study was conducted to estimate the leaf area index (LAI) and leaf nitrogen content (LNC) of wheat crops from drone-image-derived NDVI. Drone-based multispectral imaging of a wheat field with three wheat varieties (DBW-187, HD-3086, PBW-826) under eight nitrogen treatments (N0, N30, N60, N90, N120, N150, N180, N210) was completed at the flowering (90 DAS) and grain-filling stages (108 DAS), respectively. Multiple correlation analysis revealed that the squared Pearson’s correlation (R²) values of NDVI with LAI and LNC during the flowering stage were 0.78, 0.86, and 0.80 for DBW-187, HD-3086, and PBW-826, respectively, and improved to 0.89, 0.88, and 0.90 during the grain-filling stage. These results indicate a strong, positive relationship between NDVI, LAI, and LNC, which becomes stronger as the crop matures. Thus, drone remote sensing can effectively assess the biophysical variables of crops, potentially reducing the need for labor-intensive conventional methods of estimation. This study demonstrated that drone-assisted approaches can greatly enhance crop growth monitoring efficiency, offering a viable alternative to traditional methods.

Abiotic stress, primarily drought, is a major threat to crop production and food security. Plant metabolites like melatonin and myoinositol enhance stress resistance, improve growth, and promote sustainability without harming biodiversity or human health. Apple farming is crucial to the Himalayan region's economy in India. Despite using High-Density Planting (HDP) with dwarf rootstocks for better yields, these rootstocks are still sensitive to drought, impacting fruit quality and production. To address these challenges, the experiment applied exogenous melatonin (100 µM) and myoinositol (20 µM, 50 µM, 100 µM, 150 µM, 200 µM) to determine optimal doses for biochemical, morphological, and molecular studies. Five apple rootstocks were grown under polycarbonate conditions at 100% and 50% field capacities. Five treatment combinations were tested at 50% F.C., with one control at 100% F.C. Data were collected on the 5th, 10th, and 15th days of drought. The best treatment was melatonin (100 µM) + myoinositol (150 µM) (T4) at 50% F.C., showing the highest net photosynthetic rate (Pn), transpiration rate (Tr), stomatal conductance (Gs) and intercellular CO₂ (Ci) at the 5^th , 10^th and 15^th day of drought for rootstock M111 (15.55, 14.44, 12.65 Pn), (0.21, 0.18 ,0.16 Gs), (267, 278, 287 Ci), M106 (12.81 ,11.96 ,10.86 Pn), (0.17, 0.15, 0.14 Gs), (280, 298, 310 Ci), M9 (10.45 ,9.87 ,8.56 Pn) , (0.17, 0.15 ,0.13 Gs), (327, 332, 339 Ci), Bud 118 (16.78, 14.15, 13.98 Pn), (.26, .24, .23 Gs), (279, 282, 287 Ci), and M116 (13.23, 12.55, 11.53 Pn) , (.21, .19, .18 Gs), (299 ,308, 318 Ci ). Hence, the best treatment combination effectively counteracts drought-related photosynthesis inhibition.

Nanotechnology offers promising applications in agriculture, aiming to increase crop production while reducing environmental impact. Nanocarriers (NCs) enable the efficient transport of biologically active molecules, minimizing the required amount of bioactive compounds and allowing for controlled release over time. Recently, NCs have been proposed as a key technology for applying agrochemicals via unmanned aerial vehicles (UAVs). This study presents the results of using chitosan-based NCs to deliver and release natural compounds in a controlled manner, specifically extracts of Rubia tinctorum and Uncaria tomentosa, for the effective and sustainable control of phytopathogens in horticultural crops. The efficacy of NC-based treatments was demonstrated in vitro and ex situ against horticultural pathogens Botrytis cinerea, Cercospora beticola, Rhizoctonia solani, and Sclerotinia sclerotiorum. Mycelial growth inhibition values ranged from 187.5 to 375 µg/mL for NCs loaded with R. tinctorum extracts and 187.5‒500 µg/mL for those with U. tomentosa extracts. Complete plant protection of artificially inoculated sugar beet and carrot plants was achieved at doses ranging from 187.5 to 500 µg/mL, depending on the pathogen. Field tests conducted on sugar beet during the 2024 growing season yielded promising results for Cercospora beticola control. The absence of phytotoxicity and clogging problems during spray application represents a significant step towards optimizing the UAV field application of these treatments.

Innovative agricultural technologies encompass a wide range of tools and techniques aimed at improving efficiency, sustainability, and productivity in farming. This study analyzes patents related to the use of artificial intelligence (AI) in agriculture. Various patent databases were utilized, employing keywords and terms such as “AI in agriculture”, “deep learning in agriculture”, “machine learning in agriculture”, “AI applications in crop yield optimization”, “crop monitoring with AI”, and “pest and disease detection using AI.” Searches were carried out using patent titles, abstracts, and claims to ensure thorough coverage and the retrieval of pertinent data. The search results were then refined based on publication year, patent classifications, applicants, and jurisdictions. As a result, 1514 patent documents were identified. The origins of AI use in agriculture patenting can be traced back to the earliest priority date, marking 1989 as the inaugural year. Significantly, the peak of patent document activity occurred in 2023. The analysis reveals that the United States and China are the most prolific nations in patenting AI applications in agriculture. The majority of inventions involve information and communication technology tailored for agriculture, fishing, and mining. Additionally, patents in this area are related to computing arrangements based on specific computational models, particularly focusing on machine learning and neural networks inspired by biological models. This study provides a patent analysis and competitive analysis covering AI usage trends in agriculture and presents recommendations to guide the development of innovative research strategies.